Sequential Data Assimilation Techniques in Oceanography

摘要: Summary We review recent developments of sequential data assimilation techniques used in oceanography to integrate spatio-temporal observations into numerical models describing physical and ecological dynamics. Theoretical aspects from the simple case linear dynamics general nonlinear are described a geostatistical point-of-view. Current methods derived Kalman filter presented least complex most perspectives for estimation by importance resampling filters discussed. Furthermore an extension ensemble transformed Gaussian variables is illustrated using simplified model. The designed predicting over geographical regions high spatial resolution under practical constraint keeping computing time sufficiently low obtain prediction before fact. Therefore paper focuses on widely computationally efficient methods.